Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result anal
Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profile, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from different aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with different weight values. Furthermore, the impact of enhancem